Сравнение методов

	Time-varying parameter VECM	Векторная авторегрессия с время-меняющимися параметрами (TVP-VAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1999–2010	2005
Автор метода	Park & Hahn (1999); extended by Bierens & Martins (2010)	Giorgio Primiceri
Тип	Dynamic multivariate time-series model	Bayesian state-space model
Основополагающий источник	Park, J. Y., & Hahn, S. B. (1999). Cointegrating regressions with time varying coefficients. Econometric Theory, 15(5), 664–703.	Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821–852.
Другие названия	TVP-VECM, time-varying VECM, TVP cointegration model, dynamic VECM with drifting coefficients	Time-Varying Parameter Vector Autoregression, TVP-SVAR, Stochastic Coefficient VAR, Zamana Göre Değişen Parametreli VAR
Связанные	3	2
Сводка	The Time-Varying Parameter Vector Error Correction Model extends the standard VECM by allowing the adjustment speeds, cointegrating vectors, and short-run dynamics to drift over time. It captures long-run cointegrating relationships among integrated series while accommodating structural change, evolving policy regimes, and shifting economic relationships within a unified state-space framework.	TVP-VAR is a Bayesian multivariate time-series model in which both the VAR coefficients and the shock covariance matrix are allowed to evolve continuously over time as random walks. Introduced by Primiceri (2005) to study U.S. monetary policy transmission, the model captures structural changes and regime shifts without requiring ex-ante knowledge of when breaks occurred, making it indispensable for macroeconomics, finance, and any setting where economic relationships are suspected to be unstable across time.